Design of target specific peptide inhibitors using generative deep learning and molecular dynamics simulations
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Abstract
We introduce a computational approach for the design of target-specific peptides. Our method integrates a Gated Recurrent Unit-based Variational Autoencoder with Rosetta FlexPepDock for peptide sequence generation and binding affinity assessment. Subsequently, molecular dynamics simulations are employed to narrow down the selection of peptides for experimental assays. We apply this computational strategy to design peptide inhibitors that specifically target β -catenin and NF- κ B essential modulator. Among the twelve β -catenin inhibitors, six exhibit improved binding affinity compared to the parent peptide. Notably, the best C-terminal peptide binds β -catenin with an IC 50 of 0.010 ± 0.06 μM, which is 15-fold better than the parent peptide. For NF- κ B essential modulator, two of the four tested peptides display substantially enhanced binding compared to the parent peptide. Collectively, this study underscores the successful integration of deep learning and structure-based modeling and simulation for target specific peptide design.
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- Authors
- Chen, S., Lin, T., Basu, R., Ritchey, J., Wang, S., Luo, Y., Li, X., Pei, D., Kara, L. B., & Cheng, X.
- Affiliations
- The Ohio State University (US)
- Carnegie Mellon University (US)
- Nantong University (CN)
- Corresponding author
- Pei, Dehua
- Kara, Levent Burak
- Cheng, Xiaolin
- Venue
- Nature Communications
- Published
- 2024-02-21
- Volume
- 15
- Issue
- 1
- Pages
- 1611-1611
- ISSN
- 2041-1723 (ISSN-L)
- PMC
- PMC10882002
- OpenAlex
- W4392091326
- Cited by
- 324
- References
- 78
- Citations by year
- 2026: 121 · 2025: 158 · 2024: 44 · 2023: 1
- FWCI
- 68.33
- Type
- journal article
- Language
- en
- Open access
- gold
- doi.org
- Locations
- Nature Communications (publishedVersion, cc-by)
- Research Square (acceptedVersion, cc-by)
- PubMed Central (publishedVersion, cc-by-nc)
- PubMed (publishedVersion)
- RePEc: Research Papers in Economics (submittedVersion)
- DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion)
- Funders
- Ohio State University (P30CA016058)
- National Institutes of Health (1s10rr025660-01a1)
- National Cancer Institute (CA234124, R01CA234124, P30CA016058)
- National Institute of General Medical Sciences (R35GM122459)
- National Center for Research Resources (S10 RR025660)
- Topics
- Physical Sciences › Computer Science › Computational Theory and Mathematics › Computational Drug Discovery Methods
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Chemical Synthesis and Analysis
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Protein Structure and Dynamics
- Keywords
- β-catenin, NF-κB essential modulator, peptide sequence generation, binding affinity prediction, molecular dynamics simulation, structure-based drug design
- MeSH terms
- Deep Learning, Peptides, Protein Binding, NF-kappa B, beta Catenin, Molecular Dynamics Simulation
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
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